Extract, Retrieve and Predict kcat values for a metabolic model to run enzyme constrained metabolic pipelines.
Project description
WILDkCAT
WILDkCAT is a set of scripts designed to extract, retrieve, and predict enzyme turnover numbers (kcat) for genome-scale metabolic models.
Installation
- Clone the repository:
git clone https://github.com/h-escoffier/WILDkCAT.git
cd WILDkCAT
conda env create -f environment.yml
conda activate wildkcat-env
- Provide your BRENDA login credentials and Entrez API email adress to query the BRENDA enzyme database and NCBI database.
Create a file named .env in the root of your project with the following content:
ENTREZ_EMAIL=your_registered_email@example.com
BRENDA_EMAIL=your_registered_email@example.com
BRENDA_PASSWORD=your_password
- Replace the placeholders with the credentials from the account you created on the BRENDA website.
- Make sure this file is not shared publicly (e.g., add .env to your .gitignore) since it contains sensitive information.
- The scripts will automatically read these environment variables to authenticate and retrieve kcat values.
Scripts Overview
extract_kcat.py
- Verifies whether the reaction EC number exists.
- Retains inputs where reaction-associated genes/enzymes are not supported by KEGG.
- Retains inputs where no enzymes are provided by the model.
retrieve_kcat.py
- If multiple enzymes are provided, searches UniProt for catalytic activity.
- If multiple catalytic enzymes are identified, store all.
- When multiple enzymes are found, computes identity percentages relative to the identified catalytic enzyme.
- Applies Arrhenius correction to values within the appropriate pH range.
- For rows with multiple scores, selects:
- The best score
- The highest identity percentage
- The lowest kcat value
predict_kcat.py
- If multiple enzymes are provided, searches UniProt for catalytic activity.
- Skips entries missing KEGG compound IDs.
TODO
- generate_reports.py: Optimize the functions from generate_reports.py to have html plots
- catapro.py: Uniprot quieries can be send as batches
- sabio_rk_api.py: Fix SABIO-RK
- catapro.py: Move PubChem API queries to a dedicated module ?
- predict_kcat.py : Integrate TurNuP kcat prediction
- generate_reports.py - general_report(): Add the coverage of the reactions with kcat values in the model
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file wildkcat-0.0.1.tar.gz.
File metadata
- Download URL: wildkcat-0.0.1.tar.gz
- Upload date:
- Size: 33.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.11.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
80167c07c483028d6fa34b60b51bc51e5a5fb94158a7fc7e611d64ad309dc651
|
|
| MD5 |
ca5561c6ffde27dd569acf2dab595f1f
|
|
| BLAKE2b-256 |
958c091250d5301c6ef4a381cb4d0b690dda776bcff4e0f4ab25828fef0bb51d
|
File details
Details for the file wildkcat-0.0.1-py3-none-any.whl.
File metadata
- Download URL: wildkcat-0.0.1-py3-none-any.whl
- Upload date:
- Size: 39.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.11.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d45b797fd660e8a02c21567b32807f27d258df39363af708478f63ff4adc736a
|
|
| MD5 |
a8f6efb92aa6a69e7f0c61ff7b0656a4
|
|
| BLAKE2b-256 |
d6d5f78aeb6992f426f483f0df71ba038290d9dd5e955017aee77a92d16bf289
|